A Global Linear Method for Camera Pose Registration



We present a linear method for global camera pose registration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical 'unbalanced scale' problem in linear methods relying on pairwise translation direction constraints, i.e. an algebraic error; nor the system degeneracy from collinear motion. In the case of three cameras, our method provides a good linear approximation of the trifocal tensor. It can be directly scaled up to register multiple cameras. The results obtained are accurate for point triangulation and can serve as a good initialization for final bundle adjustment. We evaluate the algorithm performance with different types of data and demonstrate its effectiveness. Our system produces good accuracy, robustness, and outperforms some well-known systems on efficiency.

Online Video

NUS Campus

Camera poses are computed using our method and quasi-dense points are generated by CMVS [Furukawa et al. 2010] for better visualization.


"A Global Linear Method for Camera Pose Registration" Nianjuan Jiang*, Zhaopeng Cui* and Ping Tan. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, Dec. 2013.

Oral presentation: Powerpoint PPTX slides (6.81MB)

Demo and binary code